Prevalent methods for spiking neural network simulation are neuron based, such as the method described in Literature Reference No. 1 in the List of Incorporated Literature References. There is a long-held belief that a spike neural network is limited by how fast one can simulate spike communication in synapses. Traditional research on large scale spiking neural networks focuses on general-purpose simulation, such as simulating a specific brain function within a population or populations of neurons. The research emphasizes bio-fidelity and computational efficiency of the neuron models, but still maintains maximum flexibility, which neuron-based methods provide. Memory and communication are two important bottlenecks that these simulation methods encounter in scaling up the size of the network they can simulate.
Furthermore, large scale simulations of spiking neural networks were not possible until recently with the introduction of high density computer clusters using commodity computer processors. Applications to solving real life problems (such as computer vision) are still unreachable due, in part, to the size of neuron populations required for simulation in such applications. Previous neuron-oriented simulation methods have memory complexity that is linear in the number of synapses for neurons. Additionally, previous methods involve inter-neuron communication.
Thus, a continuing need exists for a system that enables efficient simulation of spiking neural networks that can scale to very large networks with the least hardware restriction.